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== Download EMMAX ==
 
== Download EMMAX ==
The latest release of EMMAX can be downloaded at [http://www.sph.umich.edu/csg/kang/emmax/download/index.html EMMAX Download Page]
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The latest release of EMMAX can be downloaded at [http://csg.sph.umich.edu//kang/emmax/download/index.html EMMAX Download Page]
    
== Key Instructions ==
 
== Key Instructions ==
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Reformat the phenotype files in the same order of .tfam files. The phenotype file has three entries at each line, FAMID, INDID, and phenotype values. Missing phenotype values should be represented as "NA". It is simpler to regress out the covariates when generating the phenotypes, but it is possible to simultaneously adjust for covariates.
 
Reformat the phenotype files in the same order of .tfam files. The phenotype file has three entries at each line, FAMID, INDID, and phenotype values. Missing phenotype values should be represented as "NA". It is simpler to regress out the covariates when generating the phenotypes, but it is possible to simultaneously adjust for covariates.
 
Sample lines of phenotype files. (tab or space delimited)
 
Sample lines of phenotype files. (tab or space delimited)
  59811 859811 0.609109817670387  
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  859811 859811 0.609109817670387  
 
  862311 862311 -0.0735227335684144  
 
  862311 862311 -0.0735227335684144  
 
  864111 864111 -0.210247209814720
 
  864111 864111 -0.210247209814720
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  % emmax -v -d 10 -t [tped_prefix] -p [pheno_file] -k [kin_file] -o [out_prefix]
 
  % emmax -v -d 10 -t [tped_prefix] -p [pheno_file] -k [kin_file] -o [out_prefix]
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This will generate the following files:* [out_prefix].reml : REML output with 6 lines, where each line represents (1) Log-likelihood with variance component (2) Log-likelihood without variance component, (3) \delta = \sigma_e^2 / \sigma_g^2 (Ratio between variance parameters) (4) \sigma_g^2 (genetic variance parameter), and (5) \sigma_e^2 (residual variance parameter), and (6) The pseudo-heritability estimates
 * [out_prefix].ps : Each line consist of [SNP ID], [beta], [SE-beta], [p-value].
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This will generate the following files:
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* [out_prefix].reml : REML output with 6 lines, where each line represents  
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*#Log-likelihood with variance component  
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*#Log-likelihood without variance component
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*# \delta = \sigma_e^2 / \sigma_g^2 (Ratio between variance parameters)
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*# \sigma_g^2 (genetic variance parameter)
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*# sigma_e^2 (residual variance parameter)
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*# The pseudo-heritability estimates
. (Explained variance by the kinship matrix)
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* [out_prefix].ps : Each line consist of  
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*# SNP ID
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*# Beta (1 is effect allele)
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*# SE(beta)
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*# p-value.
    
=== Incorporating Covariates ===
 
=== Incorporating Covariates ===
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=== Effect allele ===
 
=== Effect allele ===
 
Q. Which allele is effect allele?
 
Q. Which allele is effect allele?
A. EMMAX simply follows the encoding scheme of .tped file in additive model. So whichever allele encoded as 1 in the .tped file, it will be the effect allele (usually the major allele)
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A. EMMAX simply follows the encoding scheme of .tped file in additive model. So whichever allele encoded as 2 in the .tped file, it will be the effect allele (usually the major allele)
    
=== Support for VCF format and Gene-level Burden Test ===
 
=== Support for VCF format and Gene-level Burden Test ===
 
Q. Is there a version supporting VCF format and gene-level burden test?
 
Q. Is there a version supporting VCF format and gene-level burden test?
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A. Use [[EPACTS]] software pipeline for running EMMAX with VCF files, including the implementations of gene-level burden tests.
 
A. Use [[EPACTS]] software pipeline for running EMMAX with VCF files, including the implementations of gene-level burden tests.
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=== Encoding Case-control Phenotypes ===
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Q. I would like to run EMMAX for case-control phenotypes. How can I encode the phenotypes?
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A. If you encode case/control to 2/1, you will be able to run case-control analysis. Because EMMAX is based on linear mixed model rather than generalized mixed model, the effect size (beta) would not be meaningful, but the p-values should be reliable (unless case/control counts are highly imbalanced).
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=== IBS matrix or BN matrix? ===
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Q. Between IBS and BN matrix, which one is preferred?
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A. We believe that BN matrix is more robust to construct the empirical kinship matrix. Also we recommend to use call rate 95% threshold and MAF threshold of 0.01 when preparing the data.
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=== NaN in the kinship file ===
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Q. I am observing a series of -nan in the kinship matrix. What is the problem?
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A. Most likely, monomorphic SNPs would create such a problem. Typically, MAF threshold such as 1% is used.
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=== Citing EMMAX ===
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Q. How can I cite EMMAX if I used it in my research?
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A. Please see http://www.ncbi.nlm.nih.gov/pubmed/20208533, or copy the line below.
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Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42:348-54
    
== Acknowledgements ==
 
== Acknowledgements ==
 
This research was supported by National Science Foundation grants 0513612, 0731455 and 0729049, and National Institutes of Health (NIH) grants 1K25HL080079 and U01-DA024417. N.A.Z. is supported by the Microsoft Research Fellowship. H.M.K. is supported by the Samsung Scholarship, National Human Genome Research Institute grant HG00521401, National Institute for Mental Health grant NH084698 and GlaxoSmithKline. C.S. is partially supported by NIH grants GM053275-14, HL087679-01, P30 1MH083268, 5PL1NS062410-03, 5UL1DE019580-03 and 5RL1MH083268-03. N.B.F. and S.K.S. are supported by NIH grants HL087679-03, 5PL1NS062410-03, 5UL1DE019580-03 and 5RL1MH083268-03. This research was supported in part by the University of California, Los Angeles subcontract of contract N01-ES-45530 from the National Toxicology Program and National Institute of Environmental Health Sciences to Perlegen Sciences.
 
This research was supported by National Science Foundation grants 0513612, 0731455 and 0729049, and National Institutes of Health (NIH) grants 1K25HL080079 and U01-DA024417. N.A.Z. is supported by the Microsoft Research Fellowship. H.M.K. is supported by the Samsung Scholarship, National Human Genome Research Institute grant HG00521401, National Institute for Mental Health grant NH084698 and GlaxoSmithKline. C.S. is partially supported by NIH grants GM053275-14, HL087679-01, P30 1MH083268, 5PL1NS062410-03, 5UL1DE019580-03 and 5RL1MH083268-03. N.B.F. and S.K.S. are supported by NIH grants HL087679-03, 5PL1NS062410-03, 5UL1DE019580-03 and 5RL1MH083268-03. This research was supported in part by the University of California, Los Angeles subcontract of contract N01-ES-45530 from the National Toxicology Program and National Institute of Environmental Health Sciences to Perlegen Sciences.
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